Delaying deletion of a dataset

ABSTRACT

Delaying deletion of a dataset, including: associating an eradication timer with the dataset, wherein the eradication timer specifies an amount of time to delay a requested deletion of the dataset; determining that the amount of time to delay the requested deletion of the dataset should be modified; and modifying the eradication timer to specify a modified amount of time to delay the requested deletion of the dataset.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of U.S. patent applicationSer. No. 17/152,834, filed Jan. 20, 2021, which is a continuationapplication of and claims priority from U.S. Pat. No. 10,908,835, issuedFeb. 2, 2021, which is a continuation in part of U.S. patent applicationSer. No. 15/885,734, filed Jan. 31, 2018, which is a continuationapplication of and claims priority from U.S. Pat. No. 9,891,858, issuedFeb. 13, 2018, which is a continuation application of and claimspriority from U.S. Pat. No. 9,589,008, issued Mar. 7, 2017, which claimsthe benefit of U.S. Provisional Application No. 61/751,142, filed Jan.10, 2013, and is incorporated by reference herein.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 4 sets forth a flow chart illustrating an example method forreversing deletion of a virtual machine according to some embodiments ofthe present disclosure.

FIG. 5 sets forth a flow chart illustrating an additional example methodfor reversing deletion of a virtual machine according to someembodiments of the present disclosure.

FIG. 6 sets forth a flow chart illustrating an additional example methodfor reversing deletion of a virtual machine according to someembodiments of the present disclosure.

FIG. 7 sets forth a flow chart illustrating an additional example methoddelaying deletion of a dataset in accordance with some embodiments ofthe present disclosure.

FIG. 8 sets forth a flow chart illustrating an additional example methoddelaying deletion of a dataset in accordance with some embodiments ofthe present disclosure.

FIG. 9 sets forth a flow chart illustrating an additional example methoddelaying deletion of a dataset in accordance with some embodiments ofthe present disclosure.

FIG. 10 sets forth a flow chart illustrating an additional examplemethod delaying deletion of a dataset in accordance with someembodiments of the present disclosure.

FIG. 11 sets forth a flow chart illustrating an additional examplemethod delaying deletion of a dataset in accordance with someembodiments of the present disclosure.

FIG. 12 sets forth a flow chart illustrating an additional examplemethod delaying deletion of a dataset in accordance with someembodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for reversing deletion of avirtual machine in accordance with embodiments of the present disclosureare described with reference to the accompanying drawings, beginningwith FIG. 1A. FIG. 1A illustrates an example system for data storage, inaccordance with some implementations. System 100 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 100 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (IF), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘PIE’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storagecontroller 119. In one embodiment, storage controller 119A-D may be aCPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120 a-n includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to Dual PCI storagedevices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128 aand 128 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a may beoperable under direction from a storage controller 125 a to synthesizeand transfer data to be stored into Flash memory devices from data thathas been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, arecalculated version of RAM content may be transferred after a storagecontroller has determined that an operation has fully committed acrossthe storage system, or when fast-write memory on the device has reacheda certain used capacity, or after a certain amount of time, to ensureimprove safety of the data or to release addressable fast-write capacityfor reuse. This mechanism may be used, for example, to avoid a secondtransfer over a bus (e.g., 128 a, 128 b) from the storage controllers125 a, 125 b. In one embodiment, a recalculation may include compressingdata, attaching indexing or other metadata, combining multiple datasegments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage unit 152 may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA, flash memory 206, and NVRAM 204 (which issuper-capacitor backed DRAM 216, see FIGS. 2B and 2C) on a PCIe(peripheral component interconnect express) board in a chassis 138 (seeFIG. 2A). The storage unit 152 may be implemented as a single boardcontaining storage, and may be the largest tolerable failure domaininside the chassis. In some embodiments, up to two storage units 152 mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool_region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g. partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or LAN, or as some other mechanism capable oftransporting digital information between the storage system 306 and thecloud services provider 302. Such a data communications link 304 may befully wired, fully wireless, or some aggregation of wired and wirelessdata communications pathways. In such an example, digital informationmay be exchanged between the storage system 306 and the cloud servicesprovider 302 via the data communications link 304 using one or more datacommunications protocols. For example, digital information may beexchanged between the storage system 306 and the cloud services provider302 via the data communications link 304 using the handheld devicetransfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’),internet protocol (‘IP’), real-time transfer protocol (‘RTP’),transmission control protocol (‘TCP’), user datagram protocol (‘UDP’),wireless application protocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on. The shared poolof configurable resources may be rapidly provisioned and released to auser of the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on. Readers will appreciate that thecloud services provider 302 may be configured to provide additionalservices to the storage system 306 and users of the storage system 306through the implementation of additional service models, as the servicemodels described above are included only for explanatory purposes and inno way represent a limitation of the services that may be offered by thecloud services provider 302 or a limitation as to the service modelsthat may be implemented by the cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage array 306 and remote, cloud-based storage thatis utilized by the storage array 306. Through the use of a cloud storagegateway, organizations may move primary iSCSI or NAS to the cloudservices provider 302, thereby enabling the organization to save spaceon their on-premises storage systems. Such a cloud storage gateway maybe configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model, eliminating the need to install and run theapplication on local computers, which may simplify maintenance andsupport of the application. Such applications may take many forms inaccordance with various embodiments of the present disclosure. Forexample, the cloud services provider 302 may be configured to provideaccess to data analytics applications to the storage system 306 andusers of the storage system 306. Such data analytics applications may beconfigured, for example, to receive vast amounts of telemetry dataphoned home by the storage system 306. Such telemetry data may describevarious operating characteristics of the storage system 306 and may beanalyzed for a vast array of purposes including, for example, todetermine the health of the storage system 306, to identify workloadsthat are executing on the storage system 306, to predict when thestorage system 306 will run out of various resources, to recommendconfiguration changes, hardware or software upgrades, workflowmigrations, or other actions that may improve the operation of thestorage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘QLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (‘STT’) MRAM. The example storageresources 308 may alternatively include non-volatile phase-change memory(‘PCM’), quantum memory that allows for the storage and retrieval ofphotonic quantum information, resistive random-access memory (‘ReRAM’),storage class memory (‘SCM’), or other form of storage resources,including any combination of resources described herein. Readers willappreciate that other forms of computer memories and storage devices maybe utilized by the storage systems described above, including DRAM,SRAM, EEPROM, universal memory, and many others. The storage resources308 depicted in FIG. 3A may be embodied in a variety of form factors,including but not limited to, dual in-line memory modules (‘DIMMs’),non-volatile dual in-line memory modules (‘NVDIMMs’), M.2, U.2, andothers.

The storage resources 308 depicted in FIG. 3A may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, internet small computersystems interface (‘iSCSI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘SoCs’), or other formof processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques to preserve the integrity of data that is storedwithin the storage systems. Readers will appreciate that such dataprotection techniques may be carried out, for example, by systemsoftware executing on computer hardware within the storage system, by acloud services provider, or in other ways. Such data protectiontechniques can include, for example, data archiving techniques thatcause data that is no longer actively used to be moved to a separatestorage device or separate storage system for long-term retention, databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe with the storagesystem, data replication techniques through which data stored in thestorage system is replicated to another storage system such that thedata may be accessible via multiple storage systems, data snapshottingtechniques through which the state of data within the storage system iscaptured at various points in time, data and database cloning techniquesthrough which duplicate copies of data and databases may be created, andother data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’), Microsoft Azure, GoogleCloud Platform, IBM Cloud, Oracle Cloud, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.For example, the cloud-based storage system 318 may be used to provideblock storage services to users of the cloud-based storage system 318,the cloud-based storage system 318 may be used to provide storageservices to users of the cloud-based storage system 318 through the useof solid-state storage, and so on.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (‘AMI’) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data received from the users of the cloud-basedstorage system 318 to the cloud-based storage system 318, erasing datafrom the cloud-based storage system 318, retrieving data from thecloud-based storage system 318 and providing such data to users of thecloud-based storage system 318, monitoring and reporting of diskutilization and performance, performing redundancy operations, such asRAID or RAID-like data redundancy operations, compressing data,encrypting data, deduplicating data, and so forth. Readers willappreciate that because there are two cloud computing instances 320, 322that each include the storage controller application 324, 326, in someembodiments one cloud computing instance 320 may operate as the primarycontroller as described above while the other cloud computing instance322 may operate as the secondary controller as described above. Readerswill appreciate that the storage controller application 324, 326depicted in FIG. 3C may include identical source code that is executedwithin different cloud computing instances 320, 322.

Consider an example in which the cloud computing environment 316 isembodied as AWS and the cloud computing instances are embodied as EC2instances. In such an example, the cloud computing instance 320 thatoperates as the primary controller may be deployed on one of theinstance types that has a relatively large amount of memory andprocessing power while the cloud computing instance 322 that operates asthe secondary controller may be deployed on one of the instance typesthat has a relatively small amount of memory and processing power. Insuch an example, upon the occurrence of a failover event where the rolesof primary and secondary are switched, a double failover may actually becarried out such that: 1) a first failover event where the cloudcomputing instance 322 that formerly operated as the secondarycontroller begins to operate as the primary controller, and 2) a thirdcloud computing instance (not shown) that is of an instance type thathas a relatively large amount of memory and processing power is spun upwith a copy of the storage controller application, where the third cloudcomputing instance begins operating as the primary controller while thecloud computing instance 322 that originally operated as the secondarycontroller begins operating as the secondary controller again. In suchan example, the cloud computing instance 320 that formerly operated asthe primary controller may be terminated. Readers will appreciate thatin alternative embodiments, the cloud computing instance 320 that isoperating as the secondary controller after the failover event maycontinue to operate as the secondary controller and the cloud computinginstance 322 that operated as the primary controller after theoccurrence of the failover event may be terminated once the primary rolehas been assumed by the third cloud computing instance (not shown).

Readers will appreciate that while the embodiments described aboverelate to embodiments where one cloud computing instance 320 operates asthe primary controller and the second cloud computing instance 322operates as the secondary controller, other embodiments are within thescope of the present disclosure. For example, each cloud computinginstance 320, 322 may operate as a primary controller for some portionof the address space supported by the cloud-based storage system 318,each cloud computing instance 320, 322 may operate as a primarycontroller where the servicing of I/O operations directed to thecloud-based storage system 318 are divided in some other way, and so on.In fact, in other embodiments where costs savings may be prioritizedover performance demands, only a single cloud computing instance mayexist that contains the storage controller application.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n depicted in FIG.3C may be embodied, for example, as instances of cloud computingresources that may be provided by the cloud computing environment 316 tosupport the execution of software applications. The cloud computinginstances 340 a, 340 b, 340 n of FIG. 3C may differ from the cloudcomputing instances 320, 322 described above as the cloud computinginstances 340 a, 340 b, 340 n of FIG. 3C have local storage 330, 334,338 resources whereas the cloud computing instances 320, 322 thatsupport the execution of the storage controller application 324, 326need not have local storage resources. The cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may be embodied, forexample, as EC2 M5 instances that include one or more SSDs, as EC2 R5instances that include one or more SSDs, as EC2 I3 instances thatinclude one or more SSDs, and so on. In some embodiments, the localstorage 330, 334, 338 must be embodied as solid-state storage (e.g.,SSDs) rather than storage that makes use of hard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block-storage 342, 344, 346 that is offered by the cloudcomputing environment 316. The block-storage 342, 344, 346 that isoffered by the cloud computing environment 316 may be embodied, forexample, as Amazon Elastic Block Store (‘EBS’) volumes. For example, afirst EBS volume may be coupled to a first cloud computing instance 340a, a second EBS volume may be coupled to a second cloud computinginstance 340 b, and a third EBS volume may be coupled to a third cloudcomputing instance 340 n. In such an example, the block-storage 342,344, 346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block-storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM.

In the example depicted in FIG. 3C, the cloud computing instances 340 a,340 b, 340 n with local storage 330, 334, 338 may be utilized, by cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 to service I/O operations that aredirected to the cloud-based storage system 318. Consider an example inwhich a first cloud computing instance 320 that is executing the storagecontroller application 324 is operating as the primary controller. Insuch an example, the first cloud computing instance 320 that isexecuting the storage controller application 324 may receive (directlyor indirectly via the secondary controller) requests to write data tothe cloud-based storage system 318 from users of the cloud-based storagesystem 318. In such an example, the first cloud computing instance 320that is executing the storage controller application 324 may performvarious tasks such as, for example, deduplicating the data contained inthe request, compressing the data contained in the request, determiningwhere to the write the data contained in the request, and so on, beforeultimately sending a request to write a deduplicated, encrypted, orotherwise possibly updated version of the data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. Either cloud computing instance 320, 322, in some embodiments,may receive a request to read data from the cloud-based storage system318 and may ultimately send a request to read data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338.

Readers will appreciate that when a request to write data is received bya particular cloud computing instance 340 a, 340 b, 340 n with localstorage 330, 334, 338, the software daemon 328, 332, 336 or some othermodule of computer program instructions that is executing on theparticular cloud computing instance 340 a, 340 b, 340 n may beconfigured to not only write the data to its own local storage 330, 334,338 resources and any appropriate block-storage 342, 344, 346 that areoffered by the cloud computing environment 316, but the software daemon328, 332, 336 or some other module of computer program instructions thatis executing on the particular cloud computing instance 340 a, 340 b,340 n may also be configured to write the data to cloud-based objectstorage 348 that is attached to the particular cloud computing instance340 a, 340 b, 340 n. The cloud-based object storage 348 that is attachedto the particular cloud computing instance 340 a, 340 b, 340 n may beembodied, for example, as Amazon Simple Storage Service (‘S3’) storagethat is accessible by the particular cloud computing instance 340 a, 340b, 340 n. In other embodiments, the cloud computing instances 320, 322that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the cloud-basedobject storage 348.

Readers will appreciate that, as described above, the cloud-basedstorage system 318 may be used to provide block storage services tousers of the cloud-based storage system 318. While the local storage330, 334, 338 resources and the block-storage 342, 344, 346 resourcesthat are utilized by the cloud computing instances 340 a, 340 b, 340 nmay support block-level access, the cloud-based object storage 348 thatis attached to the particular cloud computing instance 340 a, 340 b, 340n supports only object-based access. In order to address this, thesoftware daemon 328, 332, 336 or some other module of computer programinstructions that is executing on the particular cloud computinginstance 340 a, 340 b, 340 n may be configured to take blocks of data,package those blocks into objects, and write the objects to thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n.

Consider an example in which data is written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system 318 issues a request to write data that, after beingcompressed and deduplicated by the storage controller application 324,326 results in the need to write 5 MB of data. In such an example,writing the data to the local storage 330, 334, 338 resources and theblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n is relatively straightforward as5 blocks that are 1 MB in size are written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n. Insuch an example, the software daemon 328, 332, 336 or some other moduleof computer program instructions that is executing on the particularcloud computing instance 340 a, 340 b, 340 n may be configured to: 1)create a first object that includes the first 1 MB of data and write thefirst object to the cloud-based object storage 348, 2) create a secondobject that includes the second 1 MB of data and write the second objectto the cloud-based object storage 348, 3) create a third object thatincludes the third 1 MB of data and write the third object to thecloud-based object storage 348, and so on. As such, in some embodiments,each object that is written to the cloud-based object storage 348 may beidentical (or nearly identical) in size. Readers will appreciate that insuch an example, metadata that is associated with the data itself may beincluded in each object (e.g., the first 1 MB of the object is data andthe remaining portion is metadata associated with the data).

Readers will appreciate that the cloud-based object storage 348 may beincorporated into the cloud-based storage system 318 to increase thedurability of the cloud-based storage system 318. Continuing with theexample described above where the cloud computing instances 340 a, 340b, 340 n are EC2 instances, readers will understand that EC2 instancesare only guaranteed to have a monthly uptime of 99.9% and data stored inthe local instance store only persists during the lifetime of the EC2instance. As such, relying on the cloud computing instances 340 a, 340b, 340 n with local storage 330, 334, 338 as the only source ofpersistent data storage in the cloud-based storage system 318 may resultin a relatively unreliable storage system. Likewise, EBS volumes aredesigned for 99.999% availability. As such, even relying on EBS as thepersistent data store in the cloud-based storage system 318 may resultin a storage system that is not sufficiently durable. Amazon S3,however, is designed to provide 99.999999999% durability, meaning that acloud-based storage system 318 that can incorporate S3 into its pool ofstorage is substantially more durable than various other options.

Readers will appreciate that while a cloud-based storage system 318 thatcan incorporate S3 into its pool of storage is substantially moredurable than various other options, utilizing S3 as the primary pool ofstorage may result in storage system that has relatively slow responsetimes and relatively long I/O latencies. As such, the cloud-basedstorage system 318 depicted in FIG. 3C not only stores data in S3 butthe cloud-based storage system 318 also stores data in local storage330, 334, 338 resources and block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n, suchthat read operations can be serviced from local storage 330, 334, 338resources and the block-storage 342, 344, 346 resources that areutilized by the cloud computing instances 340 a, 340 b, 340 n, therebyreducing read latency when users of the cloud-based storage system 318attempt to read data from the cloud-based storage system 318.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block-storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block-storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block-storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n.

As described above, when the cloud computing instances 340 a, 340 b, 340n with local storage 330, 334, 338 are embodied as EC2 instances, thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 are only guaranteed to have a monthly uptime of 99.9% and datastored in the local instance store only persists during the lifetime ofeach cloud computing instance 340 a, 340 b, 340 n with local storage330, 334, 338. As such, one or more modules of computer programinstructions that are executing within the cloud-based storage system318 (e.g., a monitoring module that is executing on its own EC2instance) may be designed to handle the failure of one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. In such an example, the monitoring module may handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 by creating one or more new cloudcomputing instances with local storage, retrieving data that was storedon the failed cloud computing instances 340 a, 340 b, 340 n from thecloud-based object storage 348, and storing the data retrieved from thecloud-based object storage 348 in local storage on the newly createdcloud computing instances. Readers will appreciate that many variants ofthis process may be implemented.

Consider an example in which all cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 failed. In such an example, themonitoring module may create new cloud computing instances with localstorage, where high-bandwidth instances types are selected that allowfor the maximum data transfer rates between the newly createdhigh-bandwidth cloud computing instances with local storage and thecloud-based object storage 348. Readers will appreciate that instancestypes are selected that allow for the maximum data transfer ratesbetween the new cloud computing instances and the cloud-based objectstorage 348 such that the new high-bandwidth cloud computing instancescan be rehydrated with data from the cloud-based object storage 348 asquickly as possible. Once the new high-bandwidth cloud computinginstances are rehydrated with data from the cloud-based object storage348, less expensive lower-bandwidth cloud computing instances may becreated, data may be migrated to the less expensive lower-bandwidthcloud computing instances, and the high-bandwidth cloud computinginstances may be terminated.

Readers will appreciate that in some embodiments, the number of newcloud computing instances that are created may substantially exceed thenumber of cloud computing instances that are needed to locally store allof the data stored by the cloud-based storage system 318. The number ofnew cloud computing instances that are created may substantially exceedthe number of cloud computing instances that are needed to locally storeall of the data stored by the cloud-based storage system 318 in order tomore rapidly pull data from the cloud-based object storage 348 and intothe new cloud computing instances, as each new cloud computing instancecan (in parallel) retrieve some portion of the data stored by thecloud-based storage system 318. In such embodiments, once the datastored by the cloud-based storage system 318 has been pulled into thenewly created cloud computing instances, the data may be consolidatedwithin a subset of the newly created cloud computing instances and thosenewly created cloud computing instances that are excessive may beterminated.

Consider an example in which 1000 cloud computing instances are neededin order to locally store all valid data that users of the cloud-basedstorage system 318 have written to the cloud-based storage system 318.In such an example, assume that all 1,000 cloud computing instancesfail. In such an example, the monitoring module may cause 100,000 cloudcomputing instances to be created, where each cloud computing instanceis responsible for retrieving, from the cloud-based object storage 348,distinct 1/100,000th chunks of the valid data that users of thecloud-based storage system 318 have written to the cloud-based storagesystem 318 and locally storing the distinct chunk of the dataset that itretrieved. In such an example, because each of the 100,000 cloudcomputing instances can retrieve data from the cloud-based objectstorage 348 in parallel, the caching layer may be restored 100 timesfaster as compared to an embodiment where the monitoring module onlycreate 1000 replacement cloud computing instances. In such an example,over time the data that is stored locally in the 100,000 could beconsolidated into 1,000 cloud computing instances and the remaining99,000 cloud computing instances could be terminated.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Consider anexample in which the monitoring module monitors the performance of thecould-based storage system 318 via communications with one or more ofthe cloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326, via monitoringcommunications between cloud computing instances 320, 322, 340 a, 340 b,340 n, via monitoring communications between cloud computing instances320, 322, 340 a, 340 b, 340 n and the cloud-based object storage 348, orin some other way. In such an example, assume that the monitoring moduledetermines that the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318. In such anexample, the monitoring module may create a new, more powerful cloudcomputing instance (e.g., a cloud computing instance of a type thatincludes more processing power, more memory, etc. . . . ) that includesthe storage controller application such that the new, more powerfulcloud computing instance can begin operating as the primary controller.Likewise, if the monitoring module determines that the cloud computinginstances 320, 322 that are used to support the execution of a storagecontroller application 324, 326 are oversized and that cost savingscould be gained by switching to a smaller, less powerful cloud computinginstance, the monitoring module may create a new, less powerful (andless expensive) cloud computing instance that includes the storagecontroller application such that the new, less powerful cloud computinginstance can begin operating as the primary controller.

Consider, as an additional example of dynamically sizing the cloud-basedstorage system 318, an example in which the monitoring module determinesthat the utilization of the local storage that is collectively providedby the cloud computing instances 340 a, 340 b, 340 n has reached apredetermined utilization threshold (e.g., 95%). In such an example, themonitoring module may create additional cloud computing instances withlocal storage to expand the pool of local storage that is offered by thecloud computing instances. Alternatively, the monitoring module maycreate one or more new cloud computing instances that have largeramounts of local storage than the already existing cloud computinginstances 340 a, 340 b, 340 n, such that data stored in an alreadyexisting cloud computing instance 340 a, 340 b, 340 n can be migrated tothe one or more new cloud computing instances and the already existingcloud computing instance 340 a, 340 b, 340 n can be terminated, therebyexpanding the pool of local storage that is offered by the cloudcomputing instances. Likewise, if the pool of local storage that isoffered by the cloud computing instances is unnecessarily large, datacan be consolidated and some cloud computing instances can beterminated.

Readers will appreciate that the cloud-based storage system 318 may besized up and down automatically by a monitoring module applying apredetermined set of rules that may be relatively simple of relativelycomplicated. In fact, the monitoring module may not only take intoaccount the current state of the cloud-based storage system 318, but themonitoring module may also apply predictive policies that are based on,for example, observed behavior (e.g., every night from 10 PM until 6 AMusage of the storage system is relatively light), predeterminedfingerprints (e.g., every time a virtual desktop infrastructure adds 100virtual desktops, the number of IOPS directed to the storage systemincrease by X), and so on. In such an example, the dynamic scaling ofthe cloud-based storage system 318 may be based on current performancemetrics, predicted workloads, and many other factors, includingcombinations thereof.

Readers will further appreciate that because the cloud-based storagesystem 318 may be dynamically scaled, the cloud-based storage system 318may even operate in a way that is more dynamic. Consider the example ofgarbage collection. In a traditional storage system, the amount ofstorage is fixed. As such, at some point the storage system may beforced to perform garbage collection as the amount of available storagehas become so constrained that the storage system is on the verge ofrunning out of storage. In contrast, the cloud-based storage system 318described here can always ‘add’ additional storage (e.g., by adding morecloud computing instances with local storage). Because the cloud-basedstorage system 318 described here can always ‘add’ additional storage,the cloud-based storage system 318 can make more intelligent decisionsregarding when to perform garbage collection. For example, thecloud-based storage system 318 may implement a policy that garbagecollection only be performed when the number of IOPS being serviced bythe cloud-based storage system 318 falls below a certain level. In someembodiments, other system-level functions (e.g., deduplication,compression) may also be turned off and on in response to system load,given that the size of the cloud-based storage system 318 is notconstrained in the same way that traditional storage systems areconstrained.

Readers will appreciate that embodiments of the present disclosureresolve an issue with block-storage services offered by some cloudcomputing environments as some cloud computing environments only allowfor one cloud computing instance to connect to a block-storage volume ata single time. For example, in Amazon AWS, only a single EC2 instancemay be connected to an EBS volume. Through the use of EC2 instances withlocal storage, embodiments of the present disclosure can offermulti-connect capabilities where multiple EC2 instances can connect toanother EC2 instance with local storage (‘a drive instance’). In suchembodiments, the drive instances may include software executing withinthe drive instance that allows the drive instance to support I/Odirected to a particular volume from each connected EC2 instance. Assuch, some embodiments of the present disclosure may be embodied asmulti-connect block storage services that may not include all of thecomponents depicted in FIG. 3C.

In some embodiments, especially in embodiments where the cloud-basedobject storage 348 resources are embodied as Amazon S3, the cloud-basedstorage system 318 may include one or more modules (e.g., a module ofcomputer program instructions executing on an EC2 instance) that areconfigured to ensure that when the local storage of a particular cloudcomputing instance is rehydrated with data from S3, the appropriate datais actually in S3. This issue arises largely because S3 implements aneventual consistency model where, when overwriting an existing object,reads of the object will eventually (but not necessarily immediately)become consistent and will eventually (but not necessarily immediately)return the overwritten version of the object. To address this issue, insome embodiments of the present disclosure, objects in S3 are neveroverwritten. Instead, a traditional ‘overwrite’ would result in thecreation of the new object (that includes the updated version of thedata) and the eventual deletion of the old object (that includes theprevious version of the data).

In some embodiments of the present disclosure, as part of an attempt tonever (or almost never) overwrite an object, when data is written to S3the resultant object may be tagged with a sequence number. In someembodiments, these sequence numbers may be persisted elsewhere (e.g., ina database) such that at any point in time, the sequence numberassociated with the most up-to-date version of some piece of data can beknown. In such a way, a determination can be made as to whether S3 hasthe most recent version of some piece of data by merely reading thesequence number associated with an object—and without actually readingthe data from S3. The ability to make this determination may beparticularly important when a cloud computing instance with localstorage crashes, as it would be undesirable to rehydrate the localstorage of a replacement cloud computing instance with out-of-date data.In fact, because the cloud-based storage system 318 does not need toaccess the data to verify its validity, the data can stay encrypted andaccess charges can be avoided.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described above may beuseful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. AI applications may be deployed in a variety of fields,including: predictive maintenance in manufacturing and related fields,healthcare applications such as patient data & risk analytics, retailand marketing deployments (e.g., search advertising, social mediaadvertising), supply chains solutions, fintech solutions such asbusiness analytics & reporting tools, operational deployments such asreal-time analytics tools, application performance management tools, ITinfrastructure management tools, and many others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson, MicrosoftOxford, Google DeepMind, Baidu Minwa, and others. The storage systemsdescribed above may also be well suited to support other types ofapplications that are resource intensive such as, for example, machinelearning applications. Machine learning applications may perform varioustypes of data analysis to automate analytical model building. Usingalgorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed. One particular area of machine learning is referred to asreinforcement learning, which involves taking suitable actions tomaximize reward in a particular situation. Reinforcement learning may beemployed to find the best possible behavior or path that a particularsoftware application or machine should take in a specific situation.Reinforcement learning differs from other areas of machine learning(e.g., supervised learning, unsupervised learning) in that correctinput/output pairs need not be presented for reinforcement learning andsub-optimal actions need not be explicitly corrected.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may be also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks have ignited a new wave of algorithmsand tools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains. In addition to supporting the storage and use of blockchaintechnologies, the storage systems described above may also support thestorage and use of derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. As part of that process, semi-structured andunstructured data such as, for example, internet clickstream data, webserver logs, social media content, text from customer emails and surveyresponses, mobile-phone call-detail records, IoT sensor data, and otherdata may be converted to a structured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Siri, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain—computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

For further explanation, FIG. 3D illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3D, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3D, the components illustrated in FIG. 3D are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3D willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

Referring now to FIG. 4 , FIG. 5 , and FIG. 6 , one embodiment of amethod for reversing deletion of a virtual machine is shown. Thecomponents embodied in the storage system described above may generallyoperate in accordance with the method of FIG. 4 , FIG. 5 , and FIG. 6 .In addition, the steps in this embodiment are shown in sequential order.However, some steps may occur in a different order than shown, somesteps may be performed concurrently, some steps may be combined withother steps, and some steps may be absent in another embodiment.

For further explanation, FIG. 4 sets forth a flow chart illustrating anexemplary method for reversing deletion of a virtual machine accordingto embodiments of the present invention that includes managing (404), bythe storage system 402, a repository of virtual machine snapshots on adatastore. Managing (404), by the storage system 402, a repository ofvirtual machine snapshots on a datastore may be carried out bygenerating virtual machine snapshots for virtuals machines on thedatastore and storing the virtual machine snapshots in the respository.The snapshots may be a set of mediums as discussed in the parent U.S.patent application Ser. No. 14/046,876. Each snapshot of a virtualmachine may include data necessary to recreate the virtual machine as itexisted at the time the snapshot was taken. The repository itself may beany collection or grouping of data described above or in the parent U.S.patent application Ser. No. 14/046,876.

The repository of virtual machine snapshots may include virtual machinesnapshots for virtual machines that have been deleted. A virtual machinemay be deleted by a user accidentally or purposefully. A virtual machinemay also be deleted as part of a storage system process based on theattributes of the virtual machine. For example, if a virtual machineassociated with a user account that has been deactivated may be deletedas part of the deactivation. As another example, a virtual machine thathas not be accessed in a set period of time may also be deleted.

Each most recent snapshot for deleted virtual machines may remain in therepository for a set period of time (e.g., 24 hours) before thesnapshots are also deleted. During this time period, the virtual machinemay be undeleted and recovered using the snapshot. Once the set periodof time has expired, the snapshot is deleted and the virtual machine mayno longer be recovered using the snapshot.

The virtual machines hosted on the storage system 402 may include avirtual volume (e.g., a VMware vSphere® vVol) and the datastore may be avirtual volume datastore. A virtual volume is a logical storagecontainer that includes virtual machine data. A virtual volume datastoreis representation of a storage system within which virtual machines arecreated. The virtual machine snapshots in the repository of virtualmachine snapshots may be snapshots of a configuration virtual volume. Aconfiguration virtual volume is a virtual volume that includes theconfiguration information for the virtual machine. The configurationvirtual volume may be internally formatted with a file system, such as acluster file system. A snapshot of a configuration virtual volume is,therefore, a collection of data about the configuration information forthe virtual machine at the time the snapshot was created.

The method of FIG. 4 further includes receiving (406), by the storagesystem 402, a request to recover a deleted virtual machine from thedatastore. Receiving (406), by the storage system 402, a request torecover a deleted virtual machine from the datastore may be carried outby the storage system 402 receiving an indication that the a user isinitiating the recovery process for deleted virtual machines. Therequest may include an identifier of a system to host the deletedvirtual machine after recovery. The system to host the deleted virtualmachine after recovery may include a location on the storage system andhypervisor to manage the virtual machine. Alternatively, the user mayprovide the identifier of the system to host the deleted virtual machineafter the specific deleted virtual machine has been selected.

The method of FIG. 4 further includes accessing (408), by the storagesystem 402, the repository of virtual machine snapshots on the datastoreto generate a list of deleted virtual machines associated with virtualmachine snapshots in the repository of virtual machine snapshots.Generating the list of deleted virtual machines associated with virtualmachine snapshots in the repository of virtual machine snapshots may becarried out by searching the repository of virtual machine snapshots forunexpired virtual machine snapshots associated with deleted virtualmachines. As discussed above, a virtual machine snapshot is expiredafter the associated virtual machine has been deleted and a set periodof time has elapsed.

Generating the list of deleted virtual machines may also includepresenting, to the user, the list of deleted virtual machines associatedwith a virtual machine snapshot that has not expired. The list mayprompt a user to select one of the virtual machines to recover.

The method of FIG. 4 further includes receiving (410), by the storagesystem 402, a selection of one of the deleted virtual machines in thelist of deleted virtual machines. Receiving (410), by the storage system402, a selection of one of the deleted virtual machines in the list ofdeleted virtual machines may be carried out by detecting that a user hasselected one of the presented deleted virtual machines. Receiving theselection of one of the deleted virtual machines in the list of deletedvirtual machines may also be performed automatically based on a policy.Such a policy may dictate, for example, that when recovery is initiated,all virtual machines associated with unexpired virtual machine snapshotsare recovered. As another example, the policy may dictate that the mostrecently deleted virtual machine is recovered if that virtual machine isassociated with an unexpired virtual machine snapshot.

The method of FIG. 4 further includes recovering (412), by the storagesystem 402, the selected deleted virtual machine using a virtual machinesnapshot for the selected deleted virtual machine. A deleted virtualmachine refers to a virtual machine that is no longer accessible oruseable by the storage system or any other entity. Because the virtualmachine has been deleted and not temporary placed into a system recyclebin or trash can structure, recovering (412) the selected deletedvirtual machine requires utilizing the virtual machine snapshot for theselected deleted virtual machine. Recovering the deleted virtual machinemay include altering the permissions for the data underlying the virtualmachine snapshot from read-only to read-write. Recovering the deletedvirtual machine may also include initiating the execution of the virtualmachine on a target system and hypervisor.

For further explanation, FIG. 5 sets forth a flow chart illustrating afurther exemplary method for reversing deletion of a virtual machineaccording to embodiments of the present invention that includes managing(404), by the storage system 402, a repository of virtual machinesnapshots on a datastore; receiving (406), by the storage system 402, arequest to recover a deleted virtual machine from the datastore;accessing (408), by the storage system 402, the repository of virtualmachine snapshots on the datastore to generate a list of deleted virtualmachines associated with virtual machine snapshots in the repository ofvirtual machine snapshots; receiving (410), by the storage system 402, aselection of one of the deleted virtual machines in the list of deletedvirtual machines; and recovering (412), by the storage system 402, theselected deleted virtual machine using a virtual machine snapshot forthe selected deleted virtual machine.

The method of FIG. 5 differs from the method of FIG. 4 , however, inthat accessing (408), by the storage system 402, the repository ofvirtual machine snapshots on the datastore to generate a list of deletedvirtual machines associated with virtual machine snapshots in therepository of virtual machine snapshots includes calculating (502), foreach deleted virtual machine in the list of deleted virtual machines, anamount of time remaining until the associated virtual machine snapshotexpires. Calculating (502), for each deleted virtual machine in the listof deleted virtual machines, an amount of time remaining until theassociated virtual machine snapshot expires may be carried out bydetermining an amount of time that has elapsed since the virtual machineassociated with the virtual machine snapshot was deleted and subtractingthe elapsed time from the set expiration duration. For example, if theset expiration duration is 24 hours, and 8 hours have elapsed since theassociated virtual machine was deleted, the amount of time remaininguntil the snapshot expires is 16 hours. The amount of time remainingbefore expiration may be displayed in the list of deleted virtualmachines presented to the user.

For further explanation, FIG. 6 sets forth a flow chart illustrating afurther exemplary method for reversing deletion of a virtual machineaccording to embodiments of the present invention that includes managing(404), by the storage system 402, a repository of virtual machinesnapshots on a datastore; receiving (406), by the storage system 402, arequest to recover a deleted virtual machine from the datastore;accessing (408), by the storage system 402, the repository of virtualmachine snapshots on the datastore to generate a list of deleted virtualmachines associated with virtual machine snapshots in the repository ofvirtual machine snapshots; receiving (410), by the storage system 402, aselection of one of the deleted virtual machines in the list of deletedvirtual machines; and recovering (412), by the storage system 402, theselected deleted virtual machine using a virtual machine snapshot forthe selected deleted virtual machine.

The method of FIG. 6 differs from the method of FIG. 4 , however, inthat the method of FIG. 6 further includes receiving (602), by thestorage system 402, a request to generate an initial virtual machinesnapshot for a virtual machine; generating (604), by the storage system402, the initial virtual machine snapshot for the virtual machine;storing (606), by the storage system 402, the initial virtual machinesnapshot in the repository of virtual machine snapshots; and marking(608), by the storage system 402, the virtual machine as undeleteprotected.

Receiving (602), by the storage system 402, a request to generate aninitial virtual machine snapshot for a virtual machine may be carriedout by the storage system detecting user input indicating that asnapshot of a virtual machine is to be taken. Generating the initialvirtual machine snapshot may also be done automatically by the storagesystem 402 in response to a snapshot policy. For example, the snapshotpolicy may dictate that snapshots of some or all virtual machines in adatastore are taken at regular intervals.

Generating (604), by the storage system 402, the initial virtual machinesnapshot for the virtual machine and storing (606), by the storagesystem 402, the initial virtual machine snapshot in the repository ofvirtual machine snapshots may be carried out by copying data from thevirtual machine and associated files to a new virtual machine snapshotcreated within the repository of virtual machine snapshots. The initialvirtual machine snapshot may include metadata describing the creation ofthe snapshot, such as the time of creation and an identifier of theassociated virtual machine.

Marking (608), by the storage system 402, the virtual machine asundelete protected may be carried out by indicating in a location withinor associated with the virtual machine that there exists at least onevirtual machine snapshot for the virtual machine. Subsequent virtualmachine snapshots for the virtual machine may be created in a similarprocess to that described above. In the event that the virtual machineis deleted, all associated virtual machine snapshots may also be deletedexcept for the most recent virtual machine snapshot. The most recentvirtual machine snapshot may remain until the expiration of the setduration of time.

For further explanation, FIG. 7 sets forth a flow chart illustrating anexample method delaying deletion of a dataset (718) in accordance withsome embodiments of the present disclosure. The dataset (718) depictedin FIG. 7 may be embodied, for example, as one or more volumes, as oneor more portions of a volume, as one or more objects, as one or morefiles, as one or more file systems, as one or more write groups, or insome other way. The dataset (718) depicted in FIG. 7 in contained withina data store (720), which may be embodied as one or more storage systemssuch as the storage systems described above with reference to FIGS.1A-3D, including modifications or combinations thereof. In the exampledepicted in FIG. 7 , the deletion of a particular dataset (718) may bedelayed for a variety of reasons. The deletion of a particular dataset(718) may be delayed, for example, to prevent the accidental orunintentional deletion of the dataset.

The example method depicted in FIG. 7 includes associating (702) aneradication timer (712) with the dataset (718). An eradication timer(712) represents a timer that specifies how long the deletion of thedataset (718) should be delayed. As such, the eradication timer (712)depicted in FIG. 7 specifies an amount of time (714) to delay arequested deletion of the dataset (718). Such an eradication timer (712)may be embodied, for example, as a data structure in which the amount oftime (714) to delay a requested deletion of the dataset (718) may beexpressed in many ways. The amount of time (714) to delay a requesteddeletion of the dataset (718) may be expressed, for example, as anabsolute amount of time (e.g., 24 hours) until the dataset (718) may bedeleted, as a specific time (e.g., 8 PM On Wednesday) after which thedataset (718) may be deleted, as a time relative to some centralizedclock after which the dataset (718) may be deleted, as a range of timeafter or during which the dataset (718) may be deleted, or in some otherway.

In the example method depicted in FIG. 7 , associating (702) aneradication timer (712) with the dataset (718) may be carried out in avariety of ways. For example, a list (or some other data structure) maybe maintained that associates an identification of a particulareradication timer (712) with an identifier for a particular dataset(718). In other embodiments, the timing information associated with theextent to which the deletion of a particular dataset (718) may beassociated with an identifier for the particular dataset (718). Forexample, a list (or other data structure) may be maintained thatassociates an an identifier for each dataset (718) whose deletion needsto be delayed for no longer than 15 minutes with a timing value of 15minutes, an identifier for each dataset (718) whose deletion needs to bedelayed for no longer than 15-30 minutes with a timing value of 30minutes, an identifier for each dataset (718) whose deletion needs to bedelayed for no longer than 30-60 minutes with a timing value of 60minutes. In such an example, once the timing value described in theprevious sentence has expired, the associated dataset (718) may be acandidate for deletion. In other embodiments, metadata may be associatedwith or attached to the dataset (718) itself to store the timinginformation contained in the eradication timer (712).

The example method depicted in FIG. 7 also includes determining (704)that the amount of time (714) to delay the requested deletion of thedataset (718) should be modified. Determining (704) that the amount oftime (714) to delay the requested deletion of the dataset (718) shouldbe modified may be carried out, for example, in response to input froman administrator or other user attempting to modify the amount of timethat a requested deletion of a dataset should be increased or decreased.In other embodiments, determining (704) that the amount of time (714) todelay the requested deletion of the dataset (718) should be modified maybe carried out through the use of one or more policies. For example, aparticular policy may cause the amount of time (714) to delay therequested deletion of the dataset (718) that is specified by aparticular eradication timer (712) to be a function of how full (from acapacity perspective) the storage system is that stores that datatset(718). In such an example, datasets that are stored on relatively fullstorage systems may be associated with a relatively short eradicationtimer to allow for much needed space to be freed quickly, whereasdatasets that are stored on relatively empty storage systems may beassociated with a relatively long eradication timer as the storagesystem has plenty of free space and does not need to rush to perform adeletion of the dataset (718). Other policies may be implemented inother embodiments, and applying such policies may result in determining(704) that the amount of time (714) to delay the requested deletion ofthe dataset (718) should be modified.

The example method depicted in FIG. 7 also includes modifying (710) theeradication timer (712) to specify a modified amount of time (716) todelay the requested deletion of the dataset (718). Modifying (710) theeradication timer (712) to specify a modified amount of time (716) todelay the requested deletion of the dataset (718) may be carried out,for example, by modifying one or more of the data structures describedabove that are used to support the eradication timer (712). In such anexample, such a modification may be subject to various conditions asdescribed below. For example, if a request to delete the dataset (718)has already been received and the amount of time (714) specified in theeradication timer (712) has not yet expired, modifying (710) theeradication timer (712) to specify a modified amount of time (716) thatis shorter (i.e., that would result in more rapid deletion of thedataset) than the original amount of time (714) specified in theeradication timer (712) may be delayed. Other embodiments will bedescried in greater detail below. In the example depicted in FIG. 7 ,modifying (710) the eradication timer (712) to specify a modified amountof time (716) to delay the requested deletion of the dataset (718) maybe carried out in response to affirmatively (706) determining (704) thatthe amount of time (714) to delay the requested deletion of the dataset(718) should be modified. If a determination is made that the amount oftime (714) to delay the requested deletion of the dataset (718) shouldnot (708) be modified, the example method depicted in FIG. 7 maycontinue by later making another determination as to whether the amountof time (714) to delay the requested deletion of the dataset (718)should be modified.

For further explanation, FIG. 8 sets forth a flow chart illustrating anadditional example method delaying deletion of a dataset (718) inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 8 is similar to the example method depicted inFIG. 7 as the example method depicted in FIG. 8 also includesassociating (702) an eradication timer (712) with the dataset (718),determining (704) that the amount of time (714) to delay the requesteddeletion of the dataset (718) should be modified, and modifying (710)the eradication timer (712) to specify a modified amount of time (716)to delay the requested deletion of the dataset (718).

The example method depicted in FIG. 8 also includes receiving (802) arequest to delete the dataset (718). The request to delete the dataset(718) may be received (802), for example, from a user that issues arequest to delete the dataset (718), from an application that issues arequest to delete the dataset (718), or from some other entity.

The example method depicted in FIG. 8 also includes retaining (804) thedataset (718) until expiration of the eradication timer (712). Morespecifically, the dataset (718) may be retained (804) until expirationof the amount of time (714) that is included in the eradication timer(712). In such an example, receiving (802) the request to delete thedataset (718) may cause the process of the eradication timer (712)expiring to begin. For example, if the amount of time (714) that isincluded in the eradication timer (712) indicates that deletion of thedataset (718) should be delayed for 24 hours, receiving (802) therequest to delete the dataset (718) may causes the process of the 24hour period to begin to expire. In such an example, the dataset (718)will be retained for at least 24 hours, during which a user or otherentity may even determine that the dataset (718) should not actually bedeleted at all. In such a way, an unintentional or accidental deletionof the dataset (718) may be avoided.

For further explanation, FIG. 9 sets forth a flow chart illustrating anadditional example method delaying deletion of a dataset (718) inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 9 is similar to the example methods depicted inFIG. 7 and FIG. 8 , as the example method depicted in FIG. 9 alsoincludes associating (702) an eradication timer (712) with the dataset(718), determining (704) that the amount of time (714) to delay therequested deletion of the dataset (718) should be modified, modifying(710) the eradication timer (712) to specify a modified amount of time(716) to delay the requested deletion of the dataset (718), receiving(802) a request to delete the dataset (718), and retaining (804) thedataset (718) until expiration of the eradication timer (712).

The example method depicted in FIG. 9 also includes determining (902)whether a remaining amount of time to delay a requested deletion of thedataset (718) is greater than the modified amount of time (716) to delaythe requested deletion of the dataset (718). The remaining amount oftime to delay a requested deletion of the dataset (718) may becalculated based on the original amount of time (714) that was specifiedin an eradication timer (712), adjusted to account for time that hadlapsed since a request to delete the dataset (718) had been received(802). Determining (902) whether the remaining amount of time to delaythe requested deletion of the dataset (718) is greater than the modifiedamount of time (716) to delay the requested deletion of the dataset(718) may be carried out to determine whether using the modified amountof time (716) to delay the requested deletion of the dataset (718) wouldallow the dataset (718) to be deleted prior to the point in time wherethe original amount of time (714) to delay the requested deletion of thedataset (718) would have lapsed.

Consider an example in which a dataset (718) was written to the datastore (720), the original amount of time (714) to delay the requesteddeletion of the dataset (718) was set to 24 hours, and a request todelete the dataset (718) was subsequently received (802). In thisexample, further assume that 2 hours after the request to delete thedataset (718) was received (802), it was determined (704) that theamount of time (714) to delay the requested deletion of the dataset(718) should be modified to 12 hours. In such an example, the remainingamount of time (714) to delay the requested deletion of the dataset(718) would be 22 hours. If the eradication timer (712) were modified(710) to specify a modified amount of time (716) of 12 hours, thedataset (718) may be available for deletion 14 hours the request todelete the dataset (718) had been received received (802)—in spite ofthe fact that when the request to delete the dataset (718) wassubsequently received (802), the dataset (718) was not available to bedeleted for another 24 hours. In this example, and to avoid failing tomeet a guaranteed level of retention that was made at the time that therequest to delete the dataset (718) was subsequently received (802),deletion of the dataset (718) may continue to be delayed by the originalamount of time (714) that was specified in the eradication timer (712).As such, the example method depicted in FIG. 8 includes, responsive todetermining that the remaining amount of time to delay the requesteddeletion of the dataset (718) is greater than the modified amount oftime (716) to delay the requested deletion of the dataset (716),delaying (904) the deletion of the dataset by the remaining amount oftime to delay the requested deletion of the dataset (718).

The example method depicted in FIG. 9 also includes, responsive todetermining that the remaining amount of time to delay the requesteddeletion of the dataset (718) is not greater than the modified amount oftime (716) to delay the requested deletion of the dataset (718),delaying the deletion of dataset (718) by the modified amount of time(716) to delay the requested deletion of the dataset (718). Readers willappreciate that in the example described here, deletion of the dataset(718) may therefore be delayed by the maximum of the remaining amount oftime to delay the requested deletion of the dataset (718) and themodified amount of time (716) to delay the requested deletion of thedataset (718). In other embodiments, deletion of the dataset (718) maybe set to the minimum of the remaining amount of time to delay therequested deletion of the dataset (718) and the modified amount of time(716) to delay the requested deletion of the dataset (718).

One or more embodiments may be described herein with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

For further explanation, FIG. 10 sets forth a flow chart illustrating anadditional example method delaying deletion of a dataset (718) inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 10 is similar to the example methods depicted inFIGS. 7-9 , as the example method depicted in FIG. 10 also includesassociating (702) an eradication timer (712) with the dataset (718),determining (704) that the amount of time (714) to delay the requesteddeletion of the dataset (718) should be modified, modifying (710) theeradication timer (712) to specify a modified amount of time (716) todelay the requested deletion of the dataset (718), receiving (802) arequest to delete the dataset (718), and retaining (804) the dataset(718) until expiration of the eradication timer (712).

The example method depicted in FIG. 10 also includes detecting (1002)that the dataset (718) has become unavailable. The dataset (718) maybecome unavailable for a variety of reasons including, for example,because the data store (720) has failed or otherwise become unavailable,because networking resources that are required to access the data store(720) have failed or otherwise become unavailable, or for some otherreason. In such an example, however, proper deletion of the dataset(718) in accordance with the time constraints set forth in aneradication timer (718) would not qualify as a reason that the dataset(718) has become unavailable. In such an example, detecting (1002) thatthe dataset (718) has become unavailable by detecting that an attempt toaccess the data store (720) has failed, by detecting that the data store(720) has become unavailable through the use of a heartbeating mechanismor similar mechanism that periodically pings the data store (720) toverify its availability, or in some other way.

The example method depicted in FIG. 10 also includes pausing (1004)expiration of the eradication timer (712) until the dataset (718) hasbecome available again. Pausing (1004) expiration of the eradicationtimer (712) until the dataset (718) has become available again may becarried out to ensure that a dataset (718) that is not available doesnot end up being deleted without the dataset (718) ever becomingavailable again prior to deletion, or to otherwise prevent the periodfor retaining the dataset (718) after a request to delete the dataset(718) has been received from being shortened without the dataset (718)actually being available.

For further explanation, FIG. 11 sets forth a flow chart illustrating anadditional example method delaying deletion of a dataset (718) inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 11 is similar to the example methods depicted inFIGS. 7-10 , as the example method depicted in FIG. 11 also includesassociating (702) an eradication timer (712) with the dataset (718),determining (704) that the amount of time (714) to delay the requesteddeletion of the dataset (718) should be modified, modifying (710) theeradication timer (712) to specify a modified amount of time (716) todelay the requested deletion of the dataset (718), receiving (802) arequest to delete the dataset (718), and retaining (804) the dataset(718) until expiration of the eradication timer (712).

The example method depicted in FIG. 11 also includes detecting (1102)that the eradication timer (712) has expired. Detecting (1102) that theeradication timer (712) has expired may be carried out, for example, bydetecting that an amount of time to delay a requested deletion haslapsed, by detecting that a date and time that a requested deletionshould be delayed until has arrived, or in some other way. As discussedabove, under normal circumstances, detecting (1102) that the eradicationtimer (712) has expired would make the associated dataset (718) acandidate for deletion.

The example method depicted in FIG. 11 also includes determining (1104),based on a retention policy, that the dataset (718) can be retainedbeyond the expiration of the eradication timer (712). Such a retentionpolicy may include one or more rules, hueristics, or other mechanismsthat may be used to retain a particular dataset (718) beyond theexpiration of the eradication timer (712). For example, a retentionpolicy may specify that if an attempt to access a dataset (718) isreceived after the expiration of the eradication timer (712) but priorto the actual deletion of the dataset (718), the dataset (718) may beretained as the attempted access may be evidence that the request todelete the dataset (718) may have been unintentional or erroneous.Likewise, a retention policy may include rules that take intoconsideration the operating characteristics of the data store,characteristics of the dataset itself, user activity, or otherconsiderations.

The example method depicted in FIG. 11 also includes continuing (1106)to delay the deletion of the dataset (718) after the expiration of theeradication timer (712). In such an example, delay of the deletion ofthe dataset (718) after the expiration of the eradication timer (712)may be continued (1106) indefinitely, for a predetermined period oftime, until confirmation that requested deletion is to proceed, or insome other way. In fact, the precise nature of the continued delay maybe specified in the retention policy itself and may even be dynamic innature (e.g., a retention policy may specify that the deletion of thedataset may continued to be delayed until the data store is 65% full).

For further explanation, FIG. 12 sets forth a flow chart illustrating anadditional example method delaying deletion of a dataset (718) inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 12 is similar to the example methods depicted inFIGS. 7-11 , as the example method depicted in FIG. 12 also includesassociating (702) an eradication timer (712) with the dataset (718),determining (704) that the amount of time (714) to delay the requesteddeletion of the dataset (718) should be modified, modifying (710) theeradication timer (712) to specify a modified amount of time (716) todelay the requested deletion of the dataset (718), receiving (802) arequest to delete the dataset (718), and retaining (804) the dataset(718) until expiration of the eradication timer (712).

The example method depicted in FIG. 12 also includes receiving (1202) arequest to restore the dataset (718) prior to the expiration of theeradication timer (712). The request to restore the dataset (718) may bereceived (802), for example, from the user that issued the request todelete the dataset (718), from an application that issued the request todelete the dataset (718), or from some other entity. In such an example,the request to restore the dataset (718) may be taken as an indicationthat the request to delete the dataset (718) was unintentional orotherwise unwanted. In alternative embodiments, rather than receiving(1202) a request to restore the dataset (718) prior to the expiration ofthe eradication timer (712), if the dataset (718) has not yet beendeleted even though the eradication timer (712) has expired, the requestto delete the dataset (718) may be disregarded as described below.

The example method depicted in FIG. 12 also includes disregarding (1204)the request to delete the dataset (718). Disregarding (1204) the requestto delete the dataset (718) may be carried out by deleted the requestfrom a queue of pending instructions, by ceasing to take further actionon the request to delete the dataset (718), or in some other way.

In the examples described, particularly with respect to the methodsdescribed in FIGS. 7-12 , determining (704) that the amount of time(714) to delay the requested deletion of the dataset (718) should bemodified may be carried out in dependence upon on one or moreeradication timer modification policies. The one or more eradicationtimer modification policies may be embodied, for example, as one or morerules, hueristics, or similar mechanism that are used to determine whena particular eradication timer (712) should be modified.

The one or more eradication timer modification policies may specify, forexample, that as capacity utilization of the data store (720) increases,the amount of time (714) specified in an eradication timer (712) shouldbe decreased. The one or more eradication timer modification policiesmay similarly specify that as capacity utilization of the data store(720) decreases, the amount of time (714) specified in an eradicationtimer (712) should be increased. In such a way, datasets that are storedin a relatively empty (from a capacity standpoint) data store may beretained for longer periods of time after a requested deletion thandatasets that are stored in a relatively full data store where availablecapacity is more limited.

In another example, the one or more eradication timer modificationpolicies may specify that as a dataset (718) becomes “hotter” in thesense that it is accessed more frequently, the amount of time (714)specified in an eradication timer (712) should be increased. The one ormore eradication timer modification policies may similarly specify thatas a dataset (718) becomes “colder” in the sense that it is accessedless frequently, the amount of time (714) specified in an eradicationtimer (712) should be decreased. In such a way, datasets that areaccessed more frequently may be retained for longer periods of timeafter a requested deletion than datasets that are accessed lessfrequently, as the consequences for an unintentional or otherwiseundesirable deletion of a frequently accessed dataset may be moreproblematic than the consequences for an unintentional or otherwiseundesirable deletion of an infrequently accessed dataset.

Readers will appreciate that other eradication timer modificationpolicies may be utilized in accordance with some embodiments of thepresent disclosure. In fact, machine learning techniques may be appliedto identify conditions that would indicate that the reversal of arequested deletion is more likely or less likely, such that eradicationtimer modification policies may be generated based on an evaluation ofone or more users actual interactions with one or more data stores. Forexample, if historical data indicates that users are more likely tounintentionally delete data that is associated with a first type ofapplication (e.g., a database application) than they are to delete datathat is associated with a second type of application (e.g., a webbrowser application), such a discovery through machine learningtechniques may result in an eradication timer modification policy wherea volume that is used by the first type of application is associatedwith an eradication timer that delays requested deletions for arelatively long period of time while a volume that is used by the secondtype of application is associated with an eradication timer that delaysrequested deletions for a relatively short period of time.

Readers will appreciate that although embodiments described hereinrelate to embodiments in which an eradication timer is associate with aparticular dataset, eradication timers may be associated with otherentities and the same techniques described above may be applied withrespect to those entities. For example, an eradication timer may beassociated with a particular storage system, with one or more storagesystems, or with any other form of a data store. In such an way, thesteps described may be applied to a particular storage system, to one ormore storage systems, or to any other form of a data store. Likewise, aneradication timer may be associated with a particular protection group,multiple protection groups, or with any other form of data storageunit(s). In such an way, the steps described may be applied to aparticular protection group, to multiple protection groups, or to anyother form of data storage unit(s) that may have one or more policies(including protection policies) applied to all data storage units. Inaddition, an eradication timer may be associated with a particular podthat includes one or more volumes that are synchronously replicatedacross multiple storage systems, an eradication timer may be associatedwith multiple data objects that reside in different data stores (e.g.,to a volume on a first storage system and a backup of said volume on asecond storage system), or data stored in multiple data stores thatshare some other relationship. In such an way, data that is stored inmultiple data stores may be subject to the same eradication timer.Readers will appreciate that eradication timers may be associated withother entities and the same techniques described above may be appliedwith respect to those entities, where all such entities are consideredto be a ‘dataset’ for the purposes of this disclosure. Stateddifferently, a ‘dataset’ may be any physical or logical collection ofdata that can be deleted.

Readers will appreciate that in some embodiments the amount of time todelay the requested deletion of a dataset, as specified in a particulareradication timer, may be negotiated by different entities. Consider anexample in which a first volume that is stored on a first storage systemis synchronously (or even asynchronously) replicated to a second storagesystem, thereby creating a second volume on the second storage system.In such an example, assume that the first volume on the first storagesystem and the second volume on the second storage system will besubjected to the same eradication timer. In such an example, to ensurethat the state of each storage system is taken into consideration, thefirst storage system and the second storage system may negotiate the theamount of time to delay the requested deletion of content in each volumeas specified in an eradication timer. Such negotiation could be carriedout, for example, by giving one system priority over the other systemwhen configuring the eradication timer, by applying some rule (e.g.,shortest time wins, longest time wins, averaging the time preferred byeach storage system), or in some other way. Alternatively, some otherentities may participate in a negotiation.

Readers will appreciate that in some embodiments, the extent to whichdata has been offloaded from a particular data store may be used toconfigure an eradication timer. For example, a first volume on a firstdata store that has been backed up or replicated to a second data storemay be associated with an eradication timer that delays a requesteddeletion from the first volume for a relatively short period of time,whereas a second volume on the same (or different) data store that hasnot been backed up or replicated to another data store may be associatedwith an eradication timer that delays a requested deletion from thesecond volume for a relatively long period of time, as the first volumemay be recovered more easily than the second volume by virtue of havingan additional copy of the first volume. Likewise, a first volume thatwas recently backed up, replicated, or snapshotted may be associatedwith an eradication timer that delays a requested deletion of the firstvolume for a relatively short period of time, whereas a second volumethat was not recently backed up, replicated, or snapshotted may beassociated with an eradication timer that delays a requested deletion ofthe second volume for a relatively long period of time, as less dataloss is likely to occur for the volume that was most recently backed up,replicated, or snapshotted. In fact, the eradication timer associatedwith a particular dataset may be shortened in response to the datasetbeing backed up, replicated, or snapshotted and the same eradicationtimer associated with the particular dataset may be lengthened as timepasses since the dataset was backed up, replicated, or snapshotted.

As discussed above, in some embodiments, system downtime may not becounted towards the expiration of the eradication timer. In other words,the eradication timer may be paused during system downtime and may onlyexpire during system uptime, such that the eradication timer only movestowards expiration when an associated dataset is accessible. In otherembodiments, the eradication timer may be associated with a distributeduptime clock, especially in embodiments where the dataset is replicatedacross multiple storage systems. Consider an example in which thedataset was synchronously replicated across multiple storage systemssuch that each storage system stored a copy of the dataset and thestorage system coordinated writes to the dataset such that the datasetsalways remained synchronized. In such an example, by using a distributeduptime clock, if one of the storage systems went offline (or the copy ofthe dataset that is stored on a particular storage system otherwisebecame unavailable), the eradication timer associated with the datasetmay pause even if the dataset is accessible on other storage systemsthat the dataset is replicated across. In such a way, the eradicationtimer may only move towards expiration when the dataset is accessible onall storage systems that participate in replicating the dataset. Inother embodiments, other thresholds may be used to determine when theeradication timer is or is not paused. For example, in some embodimentif the dataset is accessible on a majority of the storage systems theeradication timer may continue to countdown, in other embodiments if thedataset is accessible on a predetermined number of storage systems theeradication timer may continue to countdown, and so on.

In other embodiments, disaster recovery policies may impact whether theeradication timer is paused or continues to countdown. For example, ifone storage system contains a copy of the dataset and another storagesystem also contains a copy of the dataset, the eradication timer may bepaused to account for any differences in the RPO or RTO targetsassociated with the individual storage systems. In other embodiments, adistributed uptime clock may be used to operate the eradication timer insuch a way so as to meet other disaster recovery objectives.

In other embodiments, the nature of the entities that use a particulardataset may impact the use and operation of the eradication timer. Forexample, if an eradication timer is associated with a dataset that isutilized by hierarchical storage containers, the eradication timer maybe impacted by the health of the hierarchical storage containers, bypolicies associated with one or more containers in the hierarchy ofstorage containers, or otherwise tied to the hierarchical storagecontainers. For example, one or more containers in the hierarchybecoming unavailable may cause the eradication timer to be paused.Likewise, containers that are higher in the hierarchy may have anincreased ability to influence the value of the eradication timer.Readers will appreciate that in other embodiments, other relationshipsbetween the eradication timer and the storage containers may beestablished in accordance with embodiments of the present disclosure.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

While particular combinations of various functions and features of theone or more embodiments are expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method comprising: associating an eradication timer with a dataset, wherein the eradication timer specifies an amount of time to delay a requested deletion operation for the dataset, and wherein the amount of time represents a time period between a requested deletion time for the dataset and a delayed deletion time for the dataset; determining that the amount of time to delay the requested deletion operation for the dataset should be modified; and modifying the eradication timer to specify a modified amount of time to delay the requested deletion operation for the dataset.
 2. The method of claim 1 further comprising: receiving a request to delete the dataset; and retaining the dataset until expiration of the eradication timer.
 3. The method of claim 2 further comprising: determining whether a remaining amount of time to delay the requested deletion of the dataset is greater than the modified amount of time to delay the requested deletion of the dataset; responsive to determining that the remaining amount of time to delay the requested deletion of the dataset is greater than the modified amount of time to delay the requested deletion of the dataset, delaying the requested deletion of the dataset by the remaining amount of time to delay the requested deletion of the dataset; and responsive to determining that the remaining amount of time to delay the requested deletion of the dataset is not greater than the modified amount of time to delay the requested deletion of the dataset, delaying the requested deletion of the dataset by the modified amount of time to delay the requested deletion of the dataset.
 4. The method of claim 1 further comprising: detecting that the dataset has become unavailable; and pausing expiration of the eradication timer until the dataset has become available again.
 5. The method of claim 2 further comprising: detecting that the eradication timer has expired; determining, based on a retention policy, that the dataset can be retained beyond the expiration of the eradication timer; and continuing to delay the deletion of the dataset after the expiration of the eradication timer.
 6. The method of claim 2 further comprising: receiving a request to restore the dataset prior to the expiration of the eradication timer; and disregarding the request to delete the dataset.
 7. The method of claim 1, wherein determining that the amount of time to delay the requested deletion of the dataset should be modified is carried out in dependence upon on one or more eradication timer modification policies.
 8. An apparatus apparatus comprising a computer processor, a computer memory operatively coupled to the computer processor, the computer memory having disposed within it computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of: associating an eradication timer with a dataset, wherein the eradication timer specifies an amount of time to delay a requested deletion operation for the dataset, and wherein the amount of time represents a time period between a requested deletion time for the dataset and a delayed deletion time for the dataset; determining that the amount of time to delay the requested deletion operation for the dataset should be modified; and modifying the eradication timer to specify a modified amount of time to delay the requested deletion operation for the dataset.
 9. The apparatus of claim 8, further comprising computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of: receiving a request to delete the dataset; and retaining the dataset until expiration of the eradication timer.
 10. The apparatus of claim 9, further comprising computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of: determining whether a remaining amount of time to delay the requested deletion of the dataset is greater than the modified amount of time to delay the requested deletion of the dataset; responsive to determining that the remaining amount of time to delay the requested deletion of the dataset is greater than the modified amount of time to delay the requested deletion of the dataset, delaying the requested deletion of the dataset by the remaining amount of time to delay the requested deletion of the dataset; and responsive to determining that the remaining amount of time to delay the requested deletion of the dataset is not greater than the modified amount of time to delay the requested deletion of the dataset, delaying the requested deletion of the dataset by the modified amount of time to delay the requested deletion of the dataset.
 11. The apparatus of claim 8, further comprising computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of: detecting that the dataset has become unavailable; and pausing expiration of the eradication timer until the dataset has become available again.
 12. The apparatus of claim 9, further comprising computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of: detecting that the eradication timer has expired; determining, based on a retention policy, that the dataset can be retained beyond the expiration of the eradication timer; and continuing to delay the deletion of the dataset after the expiration of the eradication timer.
 13. The apparatus of claim 8, further comprising computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of: receiving a request to restore the dataset prior to an expiration of the eradication timer; and disregarding the request to delete the dataset.
 14. The apparatus of claim 8, wherein determining that the amount of time to delay the requested deletion of the dataset should be modified is carried out in dependence upon on one or more eradication timer modification policies.
 15. A non-transitory computer program product disposed upon a computer readable medium, the computer program product comprising computer program instructions that, when executed, cause a computer to carry out the steps of: associating an eradication timer with a dataset, wherein the eradication timer specifies an amount of time to delay a requested deletion operation for the dataset, and wherein the amount of time represents a time period between a requested deletion time for the dataset and a delayed deletion time for the dataset; determining that the amount of time to delay the requested deletion operation for the dataset should be modified; and modifying the eradication timer to specify a modified amount of time to delay the requested deletion operation for the dataset.
 16. The non-transitory computer program product of claim 15 further comprising computer program instructions that, when executed, cause a computer to carry out the steps of: receiving a request to delete the dataset; and retaining the dataset until expiration of the eradication timer.
 17. The non-transitory computer program product of claim 16 further comprising computer program instructions that, when executed, cause a computer to carry out the steps of: determining whether a remaining amount of time to delay the requested deletion of the dataset is greater than the modified amount of time to delay the requested deletion of the dataset; responsive to determining that the remaining amount of time to delay the requested deletion of the dataset is greater than the modified amount of time to delay the requested deletion of the dataset, delaying the requested deletion of the dataset by the remaining amount of time to delay the requested deletion of the dataset; and responsive to determining that the remaining amount of time to delay the requested deletion of the dataset is not greater than the modified amount of time to delay the requested deletion of the dataset, delaying the requested deletion of the dataset by the modified amount of time to delay the requested deletion of the dataset.
 18. The non-transitory computer program product of claim 15 further comprising computer program instructions that, when executed, cause a computer to carry out the steps of: detecting that the dataset has become unavailable; and pausing expiration of the eradication timer until the dataset has become available again.
 19. The non-transitory computer program product of claim 16 further comprising computer program instructions that, when executed, cause a computer to carry out the steps of: detecting that the eradication timer has expired; determining, based on a retention policy, that the dataset can be retained beyond the expiration of the eradication timer; and continuing to delay the deletion of the dataset after the expiration of the eradication timer.
 20. The non-transitory computer program product of claim 16 further comprising computer program instructions that, when executed, cause a computer to carry out the steps of: receiving a request to restore the dataset prior to the expiration of the eradication timer; and disregarding the request to delete the dataset. 